Sacrificial strategy to fabricate durable superhydrophobic wood via hierarchical roughness and elastic crosslinking
Bibliographic record
Abstract
With growing demand for renewable resources, superhydrophobic wood is widely regarded as leading candidate for outdoor construction applications. However, superhydrophobic wood still suffers from insufficient mechanical strength and durability under long-term abrasion and ultraviolet (UV) exposure. Herein, a sacrificial synergy strategy combining elastic cross-linked network and hierarchical roughness was proposed to enhance both toughness and durability, and its toughening mechanism is clarified. Specifically, a flexible room temperature vulcanized (RTV) silicone rubber matrix was cross-linked with vinyltriethoxysilane (VTES) to construct an elastomeric interphase, while ZnO nanorods arrays were in situ grown to generate micro/nano-scale roughness and simultaneously act as a sacrificial barrier against external abrasion. The modified wood exhibited water contact angle over 154°. Furthermore, the expected excellent robustness toward water impact (continuous 66 h), sand impingement (100 g, 35 cm, 25 cycles), mechanical abrasion (1 000 peeling cycles; sandpaper abrasion 400 cm), along with desirable chemical and environmental durability (48 h immersion in acidic and alkaline solution) of the modified wood was achieved as well. Moreover, the modified wood demonstrated multifunctional performance including self-cleaning, anti-fouling, humidity resistance (28 °C, 85% relative humidity (RH), 15 d and 35 °C, 90% RH, 6 d), and UV resistance (340 nm, 40 W, 33 d). This integrated approach not only proposed to construct mechanically resilient and environmentally durable superhydrophobic surfaces but also offered an effective design strategy for wood outdoor applications as construction material in long-term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".